Hugging Face for Natural Language Processing (NLP) Training Course

Introduction

• Overview of NLP and its applications
• Introduction to Hugging Face and its key features

Setting up a working environment

• Installing and configuring Hugging Face

Understanding the Hugging Face Transformers library and Transformer Models

• Exploring the Transformers library structure and functionalities
• Overview of various Transformer models available in Hugging Face

Utilizing Hugging Face Transformers

• Loading and using pretrained models
• Applying Transformers for various NLP tasks

Fine-Tuning a Pretrained Model

• Preparing a dataset for fine-tuning
• Fine-tuning a Transformer model on a specific task

Sharing Models and Tokenizers

• Exporting and sharing trained models
• Utilizing tokenizers for text processing

Exploring Hugging Face Datasets Library

• Overview of the Datasets library in Hugging Face
• Accessing and utilizing pre-existing datasets

Exploring Hugging Face Tokenizers Library

• Understanding tokenization techniques and their importance
• Leveraging tokenizers from Hugging Face

Carrying out Classic NLP Tasks

• Implementing common NLP tasks using Hugging Face
• Text classification, sentiment analysis, named entity recognition, etc.

Leveraging Transformer Models for Addressing Tasks in Speech Processing and Computer Vision

• Extending the use of Transformers beyond text-based tasks
• Applying Transformers for speech and image-related tasks

Troubleshooting and Debugging

• Common issues and challenges in working with Hugging Face
• Techniques for troubleshooting and debugging

Building and Sharing Your Model Demos

• Designing and creating interactive model demos
• Sharing and showcasing your models effectively

Summary and Next Steps

• Recap of key concepts and techniques learned
• Guidance on further exploration and resources for continued learning